The MEG Core staff works interactively with an extensive group of PI's in NIMH, NINDS and NIDCD for study design, task development, acquisition protocols, signal processing and data analysis. Procedures have been setup for data security, transfer and storage. We have worked with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis. This has recently been extended to include an extra-dimensional format to faciltate time based connectivity across subject groups. The instant correlation feature of AFNI is being used to investigate connectivity pattern in MEG data. Other features of AFNI that allow extraction of brain surface meshes are being used to allow more detailed modeling of the MEG source signal. Signal analysis development includes event-related SAM (synthetic aperture magnetometry) and 275 channel ICA (independent component analysis). Development of time-frequency analysis methods has included Stockwell and wavelet transforms as well as multi-taper techniques. The analysis packages have been extended to include symbolic entropy measures and a transfer entropy mutual information technique to explore brain networks. We are waiting delivery of an upgraded package that will replace the external electronics as well as an improved eye-movement monitoring system. This will substantially improve reliability and substantially increase the useful life of the system. In earlier work Brian Cornwell and colleagues have demonstrated that MEG can reliably discriminate amygdala and hippocampal signals using MEG beamforming techniques. Continuing studies have shown that hippocampal function is impaired in patients with major depression as well as other brain changes when treated with ketamine. Recent work by Cornwell et al has shown that fast gamma activity in the hippocampus correlates with spatial learning. These studies are of particular interest to possibly elucidate the mechanism of the anti-depressant action of ketamine infusion. Previous results have shown that both increased anterior cingulate activity and functional connectivity during a working memory task can predict the antidepressant response of ketamine. Zarate and colleagues have utilized MEG to show that synaptic potentiation is critical for the antidepressant action in treat resistant major depression. Studying how the brain organizes itself into functional networks is key to understanding normal human cognition as well as when it becomes disordered in mental illness. Previously, Bassett and co-workers using the spatial and temporal ability of MEG to see changes of configuration during a task, found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. They extended this work to demonstrate that dysfuctional networks can be detected and related to behavioral differences in clinical groups. We have also found differences in resting network patterns in patient groups, and have now used graph theoretical methods to examine functional networks. The interest in 'resting activity' has continued with Dr Biyu He investigating scale free properties in functional networks. Further investigations of perception and conscious awareness have been pursued by several PIs. Using MEG He and colleagues (Li, Hill, He: Spatiotemporal Dissociation of Brain Activity Underlying Subjective Awareness, Objective Performance and Confidence, The Journal of Neuroscience, 19 March 2014, 34(12)) have shown that different activities underlay these cognitive phenomena.